Non-Negative Matrix Factorization for Detection and Diagnosis of Plantwide Oscillations

Tangirala, Arun K.; Kanodia, Jitendra; Shah, Sirish L.

doi:10.1021/ie0602299

Cited by 51 publications

(39 citation statements)

References 20 publications

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“…Each principal or independent component represents a group of variables oscillating with common frequency. Tangirala et al (2007) highlighted the limitations of both PCA and ICA methods and instead proposed non-negative matrix factorization (NMF) for the dimensionality reduction. The pseudo singular values are used to reduce the dimensionality in an analogy to the use of singular value decomposition in PCA and ICA.…”

Section: Plant-wide Oscillation Detectionmentioning

confidence: 99%

“…The limitations of the PCA and ICA based method are highlighted in Tangirala et al (2007) and another method based on Non-Negative Matrix factorization (NMF) is proposed instead that is aimed at overcoming these limitations. In an analogy to the singular value decomposition performed in standard PCA, the proposed method uses the pseudo singular value decomposition (PSVD) to determine the size of basis space.…”

Section: Introductionmentioning

confidence: 99%

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A Delay Vector Variance based Approach for Detecting and Isolating the Non-linearity Induced Oscillations in Control Loops

Aftab¹,

Hovd²,

Sivalingam

2017

IFAC-PapersOnLine

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SummaryThe profitability of any industrial process is closely related to its ability to maintain near optimal operating conditions; therefore robust methods for root cause detection of any control performance degradation is crucial not only to maintain the desired operation of any industrial process but can result in marked improvement in productivity and over all economics.In a large and complex industrial control system, disturbances/oscillations originating at one point tend to propagate both up-and downstream due to underlying interactions, process flows and recycle streams, thus leading to plant-wide effects. Effective controller performance monitoring therefore requires detection and diagnosis of such plant-wide disturbances so that targeted maintenance can be achieved within the shortest possible time.Oscillations are considered to be the most important indicator of control performance degradation. Oscillations not only cause product variability and effect quality but also lead to adverse economical consequences owing to loss of precious resources like raw material and energy. Therefore, automated detection and diagnosis of oscillations needs to be reliable and effective as the large scale of the control system in industrial process plants makes manual observation and diagnosis practically impossible.Detection and diagnosis of oscillatory control loops is not trivial due to the causes such as presence of multiple oscillations, unknown process dynamics, non stationary effects and noise corruption, to name a few. The work presented in this thesis is aimed at addressing these challenges and at the development of improved tools for both detection and diagnosis of oscillations and plant-wide disturbances. Moreover, data driven methods are preferred over model based diagnostics as they are more general and are independent of a specific plant model. Dynamic proiii iv Summary cess models can be costly to develop and maintain, and for many process plants dynamic models are not available.This thesis has focussed on using the multivariate empirical mode decomposition (MEMD) and associated characteristics for the detection and diagnosis of oscillations in control loops. The methods proposed in thesis are aimed at addressing the shortcomings of the existing approaches and are fully data driven that require no a priori knowledge about the data or process itself. An improved approach for the oscillation detection has been presented that caters for non-stationary effects and reduces the mode mixing problems associated with the univariate empirical mode decomposition. Moreover, MEMD along with the proposed grouping algorithm provides a robust method to detect the plant-wide oscillations where different control variables, having common signatures of oscillations, are oscillating due to the same root cause. This helps in searching for the root cause only in the affected variables.Another important contribution of the thesis is an automated detection of harmonics in an oscillating signal. Non-linearity induced oscillations g...

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Section: Plant-wide Oscillation Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Delay Vector Variance based Approach for Detecting and Isolating the Non-linearity Induced Oscillations in Control Loops

Aftab¹,

Hovd²,

Sivalingam

2017

IFAC-PapersOnLine

View full text Add to dashboard Cite

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“…causes inconsistency to U, j 0, otherwise, (9) where i and j refer to the matrix rows and columns, respectively.…”

Section: -1-mentioning

confidence: 99%

“…Also, a variety of multivariate methods, such as principal component analysis [8] and nonnegative matrix factorization [9], have been applied to solve this diagnosis task. A recent trend has been to introduce process information into the diagnosis of plant-wide oscillations.…”

Section: Introductionmentioning

confidence: 99%

Detection and isolation of oscillations using the dynamic causal digraph method

Tikkala

Zakharov

Jämsä‐Jounela

2010

2010 Conference on Control and Fault-Tolerant Systems (SysTol)

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Abstract-This paper proposes a modification to the dynamic causal digraph (DCDG) method in order to address the detection and isolation of oscillations in a process. The proposed detection method takes advantage of the properties of residual signals generated by the DCDG method by studying their zerocrossings. The method is tested in an application to a board making process and the results are presented and discussed.

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“…Moreover, a variety of multivariate methods have been developed to decompose spectral data using for example on principal component analysis (Thornhill et al, 2002) and non-negative matrix factorization (Tangirala et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

A method for detecting non-stationary oscillations in process plants

Tikkala

Zakharov

Jämsä‐Jounela

2014

Control Engineering Practice

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This paper proposes a method for detecting oscillations in non-stationary time series based on the statistical properties of zero-crossings. The main development presented is a technique to remove non-stationary trend component from the analysed signals before applying an oscillation detection procedure. First, the method extracts the signal's baseline that is utilized to stationarize the signal. Then, an index describing the regularity of the stationarized signal's zero-crossings is computed in order to determine the presence of oscillation. The properties and performance of the method are analysed in simulation studies. Furthermore, the method is comprehensively tested with industrial data in a comparative study in which the proposed method is tested against other oscillation detection methods using industrial benchmark data and in tests on paperboard machine process . Finally, the simulation and industrial results are analysed and discussed.

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Non-Negative Matrix Factorization for Detection and Diagnosis of Plantwide Oscillations

Cited by 51 publications

References 20 publications

A Delay Vector Variance based Approach for Detecting and Isolating the Non-linearity Induced Oscillations in Control Loops

A Delay Vector Variance based Approach for Detecting and Isolating the Non-linearity Induced Oscillations in Control Loops

Detection and isolation of oscillations using the dynamic causal digraph method

A method for detecting non-stationary oscillations in process plants

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